Found by comparing what the projects do, not just their names.
Federated learner
An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization.
Federated Learning Framework
A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models
Optimization framework
This project presents optimization techniques for federated learning and minimax games in the context of machine learning
Federated optimizer
An optimization framework designed to address heterogeneity in federated learning across distributed networks
Federated Learning
This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets.
Hyperparameter optimizer
A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments.
Federated learning framework
An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation.
Federated learning frameworks
An implementation of various federated learning algorithms with a focus on communication efficiency, robustness, and fairness.
Federated learning library
An implementation of federated learning with prototype-based methods across heterogeneous clients
Federated learner
Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting.
Federated learner optimizer
A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios.
Federated Learning Optimizer
An implementation of federated learning optimized for training on renewable energy sources and spare compute capacity to minimize carbon emissions.
Federated learning method
A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness
Federated learning framework
Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.
Federated Optimization Framework
An algorithmic framework for distributed optimization that combines proximal and federated methods to improve the convergence and stability of machine learning models.